Two very important trends are alive and well in today’s B2B environment. The first is ensuring that a company has a strong digital experience for their customer base. And largely driven by interaction with a website. The second trend is using AI to serve up the right content at the right time to assist in the sales process. Both trends are being driven by the advances in the use of AI in consumer buying. We are all addicted to Netflix, Amazon, and our social media platform of choice. These platforms track our usage, serve up suggestions, and make our experience very personal. We love it. It is wonderful. It is not the same as the B2B experience. Here’s why.
A very useful way to think about data is in terms of breadth or richness as well as number of instances or sample size. Many consumer applications have very superficial breadth – where the system only captures a few items such as recent searches – but the number of instances are in the millions. Very little richness, but huge sample sizes. In these cases, domain knowledge is not important due to the sheer size of the data set. A good algorithm can easily make sense of large volumes of simple data points.
Contrast the consumer experience with that of the B2B buying / selling experience. In B2B selling, your customer can have a wonderful digital experience on your website, and you can track their interactions and learn from them. However, what their digital experience doesn’t serve up is a long list of very important items. How many people are involved in the buying decision? What is the budget? Who is the prospect working with now? Who is the incumbent? How knowledgeable is the buyer? What is their level of problem awareness of what they are trying to solve? What is their technical acumen? I could go on…
The B2B buying and selling experience is completely different than the consumer buying experience we’ve become so familiar with. In B2B sales, the buying situations sellers face are far more complex and nuanced than those faced in the consumer space. In addition to a much higher level of complexity of buying situations, we also have far fewer instances to analyze. High levels of complexity and richness and small overall instances. Any given seller will not have millions of opportunities they are pursuing. They typically don’t have hundreds. In the complex B2B sales space, a given seller might be pursuing 10 – 15 active deals at any one time. The law of large numbers will not work. Big data won’t work. What we need are better ways of analyzing buying situations that are very rich and complex, but have small sample sizes. How do we do this and how is it different than more typical AI we’re used to in our personal buying experiences?
Unfortunately, common CRM usage and note taking do not support the level of richness involved in complex B2B sales opportunities. CRM hygiene is notoriously bad. Too much of the important detail still resides in the heads of your salespeople. The challenge is how to get it out of their heads and into a mechanism that can make sense of the information and use it for predictive and enablement purposes. This is why big consulting companies make lots of money conducting interviews and analysis to get at the richness and granular details that matter when making important decisions about the sales force. These engagements are long and costly. There’s a better way.
What’s needed is an efficient way to capture the richness of details about how your buyers buy and how your sellers sell. A way that doesn’t take six months and cost $1M. Organizations can use surveys to capture granular details about the deals their sellers pursue as well as how they navigate those deals from a selling perspective. Using surveys to capture this information removes interviewer bias, speeds up the process, and increases accuracy by exploring the dynamics of real wins and losses. When you ask sellers to report what they did, you get a lot more accurate representation than when you ask sellers what they “would do” in a hypothetical situation. So, surveys that allow for forensic examination of recent deals are a great way to gather richness of detail in an efficient way.
Until the technology of CRM and the richness of B2B selling come together within the flow of work, other methods are needed to gain much needed B2B insights. Research tells us what to look for and data science helps us sort through this data once we collect it. So, what does research say and how does it impact the information we gather?
Recent research into B2B selling indicates that buying situations are not only complex, but consist of information in five primary categories:
Through many thousands of seller data points, we’ve determined that there are 17 attributes (or factors) that continuously show up in customer buying situations. These 17 attributes fall into one of the five categories. We’ve been able to reduce the number of buying attributes from the original 25 indicated by sales researchers at the Florida State University Sales Institute. We’ve examined which attributes show up most frequently and which ones don’t. By reducing the dimensionality of the buying situations, we’ve been able to improve the reliability of a machine learning algorithm that does two very important things:
Company sales forces need to go beyond the more simplistic ideas of sales personas and the customer buying journey. In our research, customer personas are only one attribute within a larger group of factors called customer dynamics. The buying journey is a misnomer. Our research reveals that each seller faces many buying journeys, not one. Leaders need better, more reliable ways to mine the data in the seller’s heads and use that data to gain deep and specific insights into how buyers buy, how that changes over time, and what sellers should do about it to win more deals.
To learn more about how VantagePoint mines seller data, please click the following link to our webinar “How Machine Learning is Changing the Game of B2B Selling.”
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